Literature DB >> 24631143

Dynamic contrast enhanced CT aiding gross tumor volume delineation of liver tumors: an interobserver variability study.

Nikolaj K G Jensen1, Danielle Mulder1, Michael Lock2, Barbara Fisher2, Rebecca Zener3, Ben Beech1, Roman Kozak4, Jeff Chen5, Ting-Yim Lee6, Eugene Wong7.   

Abstract

PURPOSE: To evaluate the application of perfusion CT for gross tumor volume (GTV) delineation for radiotherapy of intrahepatic tumors.
MATERIALS AND METHODS: 15 radiotherapy patients with confirmed liver tumors underwent contrast enhanced 4D-CT (Philips Brilliance Big-bore) as well as dynamic contrast enhanced (DCE) CT (GE 750HD). Perfusion maps were generated with CT perfusion v5 from GE. Five observers delineated GTVs of all intrahepatic foci on the 4D-CT, time-averaged DCE-CT and perfusion CT for every patient. STAPLE consensus contours were generated. Dice's coefficients were compared between GTVs generated by observers on each image set and the corresponding consensus GTVs. Comparisons were also performed with patients stratified by hepatocellular carcinoma (HCC) metastatic tumors, and by tumor volume.
RESULTS: Overall, mean Dice's coefficients were 0.81±0.14, 0.84±0.10, and 0.81±0.14 for 4D-CT, DCECT and perfusion. DCE-CT performed significantly better than 4D-CT and perfusion (p=0.005 and p=0.01 respectively). For patients with HCC, DCE-CT reduced interobserver variability significantly compared to 4D-CT (Dice's coefficients 0.87 vs. 0.84, p<0.05). For patients with metastatic disease time-averaged DCE-CT images decreased variability compared to 4D-CT (Dice's coefficient 0.81 vs. 0.76, p<0.05), especially true for tumors<100cc. The smaller tumors results are important to be included here.
CONCLUSIONS: DCE-CT imaging of liver perfusion reduced interobserver variability in GTV delineation for both HCC and metastatic liver tumors. Crown
Copyright © 2014. Published by Elsevier Ireland Ltd. All rights reserved.

Entities:  

Keywords:  4D-CT; CT perfusion; Contouring; Dynamic contrast enhanced CT; GTV delineation; Hepatocellular carcinoma

Mesh:

Substances:

Year:  2014        PMID: 24631143     DOI: 10.1016/j.radonc.2014.01.026

Source DB:  PubMed          Journal:  Radiother Oncol        ISSN: 0167-8140            Impact factor:   6.280


  15 in total

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Authors:  Michael Baumann; Mechthild Krause; Jens Overgaard; Jürgen Debus; Søren M Bentzen; Juliane Daartz; Christian Richter; Daniel Zips; Thomas Bortfeld
Journal:  Nat Rev Cancer       Date:  2016-03-18       Impact factor: 60.716

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Review 5.  The Use of Quantitative Imaging in Radiation Oncology: A Quantitative Imaging Network (QIN) Perspective.

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6.  Convolutional neural network-based automatic liver delineation on contrast-enhanced and non-contrast-enhanced CT images for radiotherapy planning.

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Journal:  Rep Pract Oncol Radiother       Date:  2020-10-02

Review 7.  Functional imaging for radiotherapy treatment planning: current status and future directions-a review.

Authors:  D Thorwarth
Journal:  Br J Radiol       Date:  2015-04-01       Impact factor: 3.039

Review 8.  Strategies to tackle the challenges of external beam radiotherapy for liver tumors.

Authors:  Michael I Lock; Jonathan Klein; Hans T Chung; Joseph M Herman; Edward Y Kim; William Small; Nina A Mayr; Simon S Lo
Journal:  World J Hepatol       Date:  2017-05-18

9.  Robust contour propagation using deep learning and image registration for online adaptive proton therapy of prostate cancer.

Authors:  Mohamed S Elmahdy; Thyrza Jagt; Roel Th Zinkstok; Yuchuan Qiao; Rahil Shahzad; Hessam Sokooti; Sahar Yousefi; Luca Incrocci; C A M Marijnen; Mischa Hoogeman; Marius Staring
Journal:  Med Phys       Date:  2019-07-12       Impact factor: 4.071

10.  A dose based approach for evaluation of inter-observer variations in target delineation.

Authors:  Ingrid Kristensen; Kristina Nilsson; Måns Agrup; Karin Belfrage; Anna Embring; Hedda Haugen; Anna-Maja Svärd; Tommy Knöös; Per Nilsson
Journal:  Tech Innov Patient Support Radiat Oncol       Date:  2017-11-04
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